GPT-4 Feedback Increases Student Activation and Learning Outcomes in Higher Education

Artificial Intelligence, in particular the novel possibilities supplied by GPT-4, is increasingly being utilized in educational settings, yet its impact on student activation and learning outcomes remains controversial. We conduct a lab-in-the-field experiment in undergraduate tutorial classes in macroeconomics at the university level. Over the course of one semester, we asked students to answer eight open-ended questions. Depending on the treatment, students received lecturer feedback at the classroom level only (LF), additional individual feedback from their peers (PF), or from the AI (AIF). We find that AIF has a significant positive effect on student activation. Compared to LF, continuous participation in the tasks is significantly higher in AIF, and it induces the longest written answers across the eight tasks. In terms of learning outcomes, AIF stands out for producing the most significant improvement in content. For the style of answers, we find no effects. We attribute the larger effects of AIF compared to PF to the higher reliability and quality of feedback provision of the AI. Our findings demonstrate that AI, specifically GPT-4, can offer a scalable and consistent solution for providing individual feedback in educational settings.